Greedy algorithms are a powerful problem-solving technique in data structures and algorithms where you make the locally optimal choice at each step, hoping it leads to a globally optimal solution. Instead of exploring every possibility like Backtracking or storing intermediate states like Dynamic Programming, greedy solutions focus on taking the best immediate decision based on the current state.
This strategy appears frequently in coding interviews because it tests your ability to recognize patterns and prove correctness. Many classic interview problems—such as activity selection, interval scheduling, and minimum cost connections—can be solved efficiently using greedy decisions combined with sorting or priority queues. In fact, greedy algorithms often pair naturally with Sorting, Array processing, and data structures like a Heap (Priority Queue).
Common greedy patterns include:
Greedy techniques are widely used in optimization and graph problems. For example, algorithms like minimum spanning tree or shortest path often rely on greedy decisions, making them closely related to concepts in Graph algorithms.
The key challenge with greedy problems is recognizing when the greedy-choice property holds—meaning a sequence of local optimal decisions leads to the overall best solution. Practicing a large variety of problems is the fastest way to build that intuition. On FleetCode, you can strengthen this skill by solving 461 Greedy problems, each designed to help you identify patterns, analyze complexity, and confidently apply greedy strategies in technical interviews.
Many greedy problems operate on arrays where elements must be processed sequentially or reordered. Understanding array traversal, indexing, and in-place updates helps implement greedy decisions efficiently.
Several graph algorithms like Prim's and certain shortest path techniques rely on greedy choices. Understanding graph structures helps apply greedy strategies to network and path optimization problems.
A large portion of greedy solutions begin by sorting elements (such as intervals or tasks). Learning sorting-based strategies helps identify optimal ordering for greedy selection.
Studying dynamic programming helps you understand when greedy works and when it fails. Many problems can be solved by both approaches, making this comparison valuable for interviews.
Priority queues allow you to repeatedly choose the best candidate at each step, which is a common greedy technique in scheduling, merging, and resource allocation problems.
| Status | Title | Solution | Practice | Difficulty | Companies | Topics |
|---|---|---|---|---|---|---|
| 3796. Find Maximum Value in a Constrained Sequence | Solution | Solve | Medium | Amazon+1 | ||
| 3457. Eat Pizzas! | Solution | Solve | Medium | Infosys | ||
| 3458. Select K Disjoint Special Substrings | Solution | Solve | Medium | Amazon | ||
| 3627. Maximum Median Sum of Subsequences of Size 3 | Solution | Solve | Medium | Amazon+1 | ||
| 3462. Maximum Sum With at Most K Elements | Solution | Solve | Medium | Amazon+1 | ||
| 3434. Maximum Frequency After Subarray Operation | Solution | Solve | Medium | Amazon+2 | ||
| 3439. Reschedule Meetings for Maximum Free Time I | Solution | Solve | Medium | Amazon+4 | ||
| 3440. Reschedule Meetings for Maximum Free Time II | Solution | Solve | Medium | Amazon+3 | ||
| 3523. Make Array Non-decreasing | Solution | Solve | Medium | Google | ||
| 3413. Maximum Coins From K Consecutive Bags | Solution | Solve | Medium | Amazon+1 | ||
| 3424. Minimum Cost to Make Arrays Identical | Solution | Solve | Medium | Amazon+1 | ||
| 3397. Maximum Number of Distinct Elements After Operations | Solution | Solve | Medium | Amazon+3 |
Start Easy, progress to Hard.
Frequently appear alongside Greedy.
Common questions about Greedy.
Greedy algorithms make the best immediate choice at each step with the goal of reaching a globally optimal solution. They work when a problem has the greedy-choice property and optimal substructure. Classic examples include activity selection, Huffman coding, and certain shortest path algorithms.
Common patterns include sorting intervals before selecting them, always picking the smallest or largest available option, using priority queues to choose the next best element, and making decisions based on earliest finish time or minimum cost.
Yes. Greedy algorithms appear regularly in FAANG and top tech interviews because they test optimization thinking and pattern recognition. While less frequent than arrays or graphs, mastering greedy problems can give you an edge in medium and hard interview questions.
Start by understanding the greedy-choice property and solving classic problems like activity selection and coin change. Then practice pattern-based questions involving sorting, heaps, and intervals. Reviewing proofs of correctness also helps build intuition for when greedy works.
Most candidates become comfortable with greedy techniques after solving 40–80 problems across different patterns such as interval selection, resource allocation, and priority queue optimization. FleetCode offers 461 greedy problems so you can gradually progress from beginner to advanced difficulty.
Popular greedy interview problems include interval scheduling, jump game, gas station, task scheduling, and minimum number of arrows to burst balloons. Practicing 30–50 well-known greedy problems usually builds enough pattern recognition for interviews.
Use a greedy approach when a locally optimal decision always leads to the globally optimal solution. If a problem requires exploring multiple states or overlapping subproblems, dynamic programming is usually a better choice.
